Release 1.1.2 RC1

Author: Songki Choi

CHANGELOG.md

@@ -2,6 +2,14 @@
 
 All notable changes to this project will be documented in this file.
 
+## \[v1.1.2\]
+
+### Bug fixes
+
+- Fix exception -> warning for anomaly dump_feature option
+- Remove `dataset.with_empty_annotations()` to keep original input structure (<https://github.com/openvinotoolkit/training_extensions/pull/1964>)
+- Fix OV batch inference (saliency map generation)  (<https://github.com/openvinotoolkit/training_extensions/pull/1965>)
+
 ## \[v1.1.1\]
 
 ### Bug fixes

README.md

@@ -5,8 +5,8 @@
 ---
 
 [Key Features](#key-features) •
-[Quick Start](https://openvinotoolkit.github.io/training_extensions/releases/1.1.1/guide/get_started/quick_start_guide/index.html) •
-[Documentation](https://openvinotoolkit.github.io/training_extensions/releases/1.1.1/index.html) •
+[Quick Start](https://openvinotoolkit.github.io/training_extensions/releases/1.1.2/guide/get_started/quick_start_guide/index.html) •
+[Documentation](https://openvinotoolkit.github.io/training_extensions/releases/1.1.2/index.html) •
 [License](#license)
 
 [![PyPI](https://img.shields.io/pypi/v/otx)](https://pypi.org/project/otx)
@@ -49,7 +49,7 @@ OpenVINO™ Training Extensions supports the following computer vision tasks:
 - **Action recognition** including action classification and detection
 - **Anomaly recognition** tasks including anomaly classification, detection and segmentation
 
-OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/releases/1.1.1/guide/explanation/algorithms/index.html):
+OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/releases/1.1.2/guide/explanation/algorithms/index.html):
 
 - **Supervised**, incremental training, which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks
 - **Semi-supervised learning**
@@ -59,17 +59,17 @@ OpenVINO™ Training Extensions will provide the following features in coming re
 
 - **Distributed training** to accelerate the training process when you have multiple GPUs
 - **Half-precision training** to save GPUs memory and use larger batch sizes
-- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/releases/1.1.1/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
+- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/releases/1.1.2/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
 - OpenVINO™ Training Extensions uses [Datumaro](https://openvinotoolkit.github.io/datumaro/docs/) as the backend to hadle datasets. Thanks to that, OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We constantly working to extend supported formats to give more freedom of datasets format choice.
-- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/releases/1.1.1/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
+- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/releases/1.1.2/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
 
 ---
 
 ## Getting Started
 
 ### Installation
 
-Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/releases/1.1.1/guide/get_started/quick_start_guide/installation.html).
+Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/releases/1.1.2/guide/get_started/quick_start_guide/installation.html).
 
 ### OpenVINO™ Training Extensions CLI Commands
 
@@ -83,7 +83,7 @@ Please refer to the [installation guide](https://openvinotoolkit.github.io/train
 - `otx demo` allows one to apply a trained model on the custom data or the online footage from a web camera and see how it will work in a real-life scenario.
 - `otx explain` runs explain algorithm on the provided data and outputs images with the saliency maps to show how your model makes predictions.
 
-You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/releases/1.1.1/guide/get_started/quick_start_guide/cli_commands.html).
+You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/releases/1.1.2/guide/get_started/quick_start_guide/cli_commands.html).
 
 ---
 

otx/__init__.py

@@ -3,7 +3,7 @@
 # Copyright (C) 2021-2023 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-__version__ = "1.1.1"
+__version__ = "1.1.2rc1"
 # NOTE: Sync w/ otx/api/usecases/exportable_code/demo/requirements.txt on release
 
 MMCLS_AVAILABLE = True

otx/api/usecases/exportable_code/demo/requirements.txt

@@ -1,4 +1,4 @@
 openvino==2022.3.0
 openmodelzoo-modelapi==2022.3.0
-otx==1.1.1
+otx==1.1.2rc1
 numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime